Morphological and Other Research Techniques for Almost Cyclic Time Series as Applied to CO2 Concentration Series

被引：4

作者：

Avilov, V. K. ^{[1
]}

Aleshnovskii, V. S. ^{[2
]}

Bezrukova, A., V ^{[2
]}

Gazaryan, V. A. ^{[2
,4
]}

Zyuzina, N. A. ^{[2
]}

Kurbatova, Yu A. ^{[1
]}

Tarbaev, D. A. ^{[2
]}

Chulichkov, A., I ^{[2
,3
]}

Shapkina, N. E. ^{[2
,5
]}

机构：

[1] Russian Acad Sci, Severtsov Inst Ecol & Evolut, Moscow 119071, Russia

[2] Lomonosov Moscow State Univ, Fac Phys, Moscow 119991, Russia

[3] Russian Acad Sci, Obukhov Inst Atmospher Phys, Moscow 119017, Russia

[4] Financial Univ Govt Russian Federat, Moscow 125993, Russia

[5] Russian Acad Sci, Inst Theoret & Appl Electromagnet, Moscow 125412, Russia

来源：

COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS | 2021年 / 61卷 / 07期

基金：

俄罗斯基础研究基金会;

关键词：

digital signal processing; quasi-periodic signals; decomposition; waveform; Fourier analysis; wavelet analysis;

D O I：

10.1134/S0965542521070046

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Based on the morphological analysis techniques developed under the guidance of Yu.P. Pyt'ev, a method for filtering time series is proposed that is capable of detecting an almost cyclic component with a varying cycle length and varying series members within cycles. The effectiveness of the approach is illustrated as applied to decomposition of time series of atmospheric CO2 concentrations. After filtering out the series component responsible for diurnal variability, the series residual becomes stationary, so mathematical statistical methods and Fourier analysis can be used for its further investigation. The results are verified by comparing them with Fourier analysis data. A cyclicity with a period longer than one day is studied using Fourier expansion and wavelet analysis of the original series.

引用

页码：1106 / 1117

页数：12

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